图神经网络应用于知识图谱构建:研究进展、农业发展潜力及未来方向
袁 欢,硕士研究生,研究方向为农业知识图谱。E-mail:yh@qs.al |
收稿日期: 2024-12-31
网络出版日期: 2025-05-14
基金资助
“十四五”国家重点研发计划项目(2023YFD2000103)
Graph Neural Networks for Knowledge Graph Construction: Research Progress, Agricultural Development Potential, and Future Directions
YUAN Huan, E-mail: yh@qs.al |
Received date: 2024-12-31
Online published: 2025-05-14
Supported by
National Key Research and Development Program Project(2023YFD2000103)
Copyright
【目的/意义】 图神经网络(Graph Neural Networks, GNN)通过图中节点的交互和消息传递来捕捉图数据的复杂关系,被广泛应用于知识图谱构建技术中知识表示、知识抽取、知识融合、知识推理等任务。农业知识图谱(Agricultural Knowledge Graph, AKG)发展及知识服务应用过程中面临数据、关系和结构复杂,以及可解释性不足等诸多挑战,GNN有望利用其在图结构数据建模中的优势破解上述难题。 【进展】 本文首先简要介绍GNN的表示方法和基本思想,并分析了典型的5种GNN模型的主体结构、特点和应用方向。随后,介绍了GNN对于知识表示、实体识别、关系抽取和事件抽取任务的技术优势,为基于多源异构大数据的AKG构建提供技术参考。此外,对GNN在知识补全融合、去噪和异常信息推断等图谱质量提升案例进行了分析。最后,介绍了GNN在农业场景中的应用和构建AKG的发展潜力。 【结论/展望】 目前,GNN在AKG构建与推理中的应用仍处于初步探索阶段,未来应重点突破跨模态数据关联分析、动态知识演化、面向场景的高效推理,以及可解释性与泛化性提升等关键技术。基于GNN的AKG构建技术,有望通过精准和细粒度的实体与关系表达与预测,为农业生产提供高效的知识服务和智慧解决方案。
袁欢 , 范蓓蕾 , 杨晨雪 , 李娴 . 图神经网络应用于知识图谱构建:研究进展、农业发展潜力及未来方向[J]. 智慧农业, 2025 , 7(2) : 41 -56 . DOI: 10.12133/j.smartag.SA202501007
[Significance] Graph neural networks (GNN) have emerged as a powerful tool in the realm of data analysis, particularly in knowledge graph construction. By capitalizing on the interaction and message passing among nodes in a graph, GNN can capture intricate relationships, making them widely applicable in various tasks, including knowledge representation, extraction, fusion, and inference. In the context of agricultural knowledge graph (AKG) development and knowledge service application, however, the agricultural domain presents unique challenges. These challenges encompass data with high multi-source heterogeneity, dynamic spatio-temporal changes in knowledge, complex relationships, and stringent requirements for interpretability. Given its strengths in graph structure data modeling, GNNs hold great promise in addressing these difficulties. For instance, in agricultural data, information from weather sensors, soil monitoring devices, and historical crop yield records varies significantly in format and type, and the ability of GNNs to handle such heterogeneity becomes crucial. [Progress] Firstly, this paper provides a comprehensive overview of the representation methods and fundamental concepts of GNNs was presented. The main structures, basic principles, characteristics, and application directions of five typical GNN models were discussed, including recursive graph neural networks (RGNN), convolutional graph neural networks (CGNN), graph auto-encoder networks (GAE), graph attention networks (GAT), and spatio-temporal graph neural networks(STGNN). Each of these models has distinct advantages in graph feature extraction, which are leveraged for tasks such as dynamic updates, knowledge completion, and complex relationship modeling in knowledge graphs. For example, STGNNs are particularly adept at handling the time-series and spatial data prevalent in agriculture, enabling more accurate prediction of crop growth patterns. Secondly, how GNN utilize graph structure information and message passing mechanisms to address issues in knowledge extraction related to multi-source heterogeneous data fusion and knowledge representation was elucidated. It can enhance the capabilities of entity recognition disambiguation and multi-modal data entity recognition. For example, when dealing with both textual descriptions of agricultural pests and corresponding image data, GNNs can effectively integrate these different modalities to accurately identify the pests. It also addresses the tasks of modeling complex dependencies and long-distance relationships or multi-modal relation extraction, achieving precise extraction of complex, missing information, or multi-modal events. Furthermore, GNNs possess unique characteristics, such as incorporating node or subgraph topology information, learning deep hidden associations between entities and relationships, generating low-dimensional representations encoding structure and semantics, and learning or fusing iterative non-linear neighborhood feature relationships on the graph structure, make it highly suitable for tasks like entity prediction, relation prediction, denoising, and anomaly information inference. These applications significantly enhance the construction quality of knowledge graphs. In an agricultural setting, this means more reliable predictions of disease outbreaks based on the relationships between environmental factors and crop health. Finally, in-depth analyses of typical cases of intelligent applications based on GNNs in agricultural knowledge question answering, recommendation systems, yield prediction, and pest monitoring and early warning are conducted. The potential of GNNs for constructing temporal agricultural knowledge models is explored, and its ability to adapt to the changing nature of agricultural data over time is highlighted. [Conclusions and Prospects] Research on constructing AKGs using GNNs is in its early stages. Future work should focus on key technologies like deep multi-source heterogeneous data fusion, knowledge graph evolution, scenario-based complex reasoning, and improving interpretability and generalization. GNN-based AKGs are expected to take on professional roles such as virtual field doctors and agricultural experts. Applications in pest control and planting decisions will be more precise, and intelligent tools like smart agricultural inputs and encyclopedia retrieval systems will be more comprehensive. By representing and predicting entities and relationships in agriculture, GNN-based AKGs can offer efficient knowledge services and intelligent solutions for sustainable agricultural development.
本研究不存在研究者以及与公开研究成果有关的利益冲突。
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